tlao-main-iCatcher
Joseph Outa
2024-02-23
0.1 Read and clean data
## [1] "/Users/jouta1/Desktop/github.nosync/tlao-public"pre-exclusion N and age
- N = 57 (tested, if went past setup and showed some parts of stimuli)
- mean age = 6.55 months
## # A tibble: 1 × 1
## mean_age
## <dbl>
## 1 6.551 Data quality checks, fixes, exclusions
2 fix s085
- s085 was erroneously coded as having LT of 0.0 by icatcher, grabbed and imputed correct data from pyhab coding (confirmed figure)
2.1 Trial Exclusions and Subject Exclusions
subjects excluded (20): - technical difficulties (5): s028, s029, s030, s042, s078 (i.e. stimuli didn’t loop, pyhab keeps freezing, no sound, ) - experimenter error (2): s035, s064 (forgetting to record shared screen, not recording session) - inattention (4): s039, s053, s058, s073 - poor video quality (1): s043 - external distraction (3): s068, s069, s075 (siblings, caregiver) - trial exclusions resulting in no trials left/accumulated looking time less than 3 seconds (5): s038, s049, s062, s067, s071 (and thus had to exclude non-consecutive test trials)
2.2 Wrangle data for plotting
2.3 Exploratory checks
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
3 Plots
3.1 Age, Conditions
## # A tibble: 1 × 1
## n
## <int>
## 1 36
## [1] 5.07 8.89## [1] 6.84
3.2 Plot all trials
3.3 Plot only test events
## # A tibble: 2 × 3
## trialType mean sd
## <chr> <dbl> <dbl>
## 1 expected 22.0 18.2
## 2 unexpected 20.2 16.24 Exploring training effect
4.1 median splitting training time
# trianing effect linear model
##
## Call:
## lm(formula = voe_effect ~ train_duration, data = d_voe)
##
## Residuals:
## Min 1Q Median 3Q Max
## -40.27 -15.48 1.21 5.70 42.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.70 4.64 0.58 0.56
## train_durationtrain shorter than median -8.16 6.22 -1.31 0.20
##
## Residual standard error: 18.5 on 34 degrees of freedom
## Multiple R-squared: 0.0482, Adjusted R-squared: 0.0202
## F-statistic: 1.72 on 1 and 34 DF, p-value: 0.1984.2 correlation between training time and voe effect
5 age differences in correlations
5.1 Plot all trials with median age split
5.2 Plot test trials
5.3 Plot test trials by condition
5.4 conditions again, different labels
5.5 Plot first test pair
6 Statistical analysis
d_icatcher_stats <- d_icatcher_plot_first_pair %>%
filter(Trials.trialType == "test") %>%
mutate(order = ifelse(condLabel %in% c(1, 4), "expected_first", "unexpected_first")) %>%
filter(!is.nan(Log_looks) & !is.infinite(Log_looks)) %>%
mutate(Index = row_number())
model <- lmer(Looks.duration_onLooks ~ trialType + (1 | SubjectInfo.subjID), data = d_icatcher_stats)
summary(model)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Looks.duration_onLooks ~ trialType + (1 | SubjectInfo.subjID)
## Data: d_icatcher_stats
##
## REML criterion at convergence: 590
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.471 -0.523 -0.352 0.494 2.029
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjectInfo.subjID (Intercept) 120 11.0
## Residual 179 13.4
## Number of obs: 71, groups: SubjectInfo.subjID, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 22.03 2.88 59.74 7.64 2.1e-10 ***
## trialTypeunexpected -1.63 3.19 34.82 -0.51 0.61
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## trlTypnxpct -0.542
#with log-transformation
model_log <- lmer(Log_looks ~ trialType + (1 | SubjectInfo.subjID) + age_months_decimal + order, data = d_icatcher_stats)
summary(model_log)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Log_looks ~ trialType + (1 | SubjectInfo.subjID) + age_months_decimal +
## order
## Data: d_icatcher_stats
##
## REML criterion at convergence: 178
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -1.7609 -0.6118 0.0184 0.5948 1.7881
##
## Random effects:
## Groups Name Variance Std.Dev.
## SubjectInfo.subjID (Intercept) 0.370 0.608
## Residual 0.408 0.639
## Number of obs: 71, groups: SubjectInfo.subjID, 36
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.6468 0.8078 33.8196 3.28 0.0024 **
## trialTypeunexpected -0.0339 0.1521 34.7899 -0.22 0.8249
## age_months_decimal 0.0275 0.1107 33.3795 0.25 0.8051
## orderunexpected_first -0.2481 0.2632 33.1789 -0.94 0.3526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) trlTyp ag_mn_
## trlTypnxpct -0.081
## ag_mnths_dc -0.973 -0.014
## ordrnxpctd_ -0.373 0.010 0.243